{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea13570e",
   "metadata": {},
   "source": [
    "## Intervening gpt-oss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b6bd3f",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/basic_tutorials/gpt-oss.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67f439a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Zhengxuan Wu and Aryaman Arora\"\n",
    "__version__ = \"08/05/2025\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16078dfc",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "This tutorial provides you the starter-kit for performing interventions on `gpt-oss` model with a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bec3b275",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    # This library is our indicator that the required installs\n",
    "    # need to be done.\n",
    "    import pyvene\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5561e1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "import pyvene\n",
    "from pyvene import top_vals, format_token\n",
    "from pyvene import RepresentationConfig, IntervenableConfig, IntervenableModel\n",
    "from pyvene import VanillaIntervention\n",
    "\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "from plotnine import (\n",
    "    ggplot,\n",
    "    geom_tile,\n",
    "    aes,\n",
    "    facet_wrap,\n",
    "    theme,\n",
    "    element_text,\n",
    "    geom_bar,\n",
    "    geom_hline,\n",
    "    scale_y_log10,\n",
    ")\n",
    "\n",
    "lsm = nn.LogSoftmax(dim=2)\n",
    "sm = nn.Softmax(dim=2)\n",
    "\n",
    "def embed_to_distrib(model, embed, log=False, logits=False):\n",
    "    \"\"\"Convert an embedding to a distribution over the vocabulary\"\"\"\n",
    "    with torch.inference_mode():\n",
    "        vocab = torch.matmul(embed, model.lm_head.weight.t())\n",
    "        if logits:\n",
    "            return vocab\n",
    "        return lsm(vocab) if log else sm(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3111c8e",
   "metadata": {},
   "source": [
    "### Factual recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df538fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital of Spain is\n",
      "_Madrid              0.7109375\n",
      "_Barcelona           0.042724609375\n",
      "_\"                   0.027587890625\n",
      "_**                  0.01904296875\n",
      "_not                 0.0157470703125\n",
      "_                    0.01080322265625\n",
      ":                    0.01080322265625\n",
      "_the                 0.01019287109375\n",
      "_a                   0.0074462890625\n",
      "_Spain               0.006988525390625\n",
      "\n",
      "The capital of Italy is\n",
      "_Rome                0.8125\n",
      "_\"                   0.02783203125\n",
      "_**                  0.01025390625\n",
      "_the                 0.0096435546875\n",
      ":                    0.00799560546875\n",
      "_a                   0.00799560546875\n",
      "_not                 0.00750732421875\n",
      "_                    0.00750732421875\n",
      "_'                   0.0037689208984375\n",
      "_known               0.0035400390625\n"
     ]
    }
   ],
   "source": [
    "config, tokenizer, gpt_oss = pyvene.create_gpt_oss()\n",
    "gpt_oss = gpt_oss.to(\"cuda\")\n",
    "\n",
    "base = \"The capital of Spain is\"\n",
    "source = \"The capital of Italy is\"\n",
    "inputs = [\n",
    "    tokenizer(base, return_tensors=\"pt\").to(\"cuda\"), \n",
    "    tokenizer(source, return_tensors=\"pt\").to(\"cuda\")\n",
    "]\n",
    "print(base)\n",
    "res = gpt_oss(**inputs[0], output_hidden_states=True)\n",
    "distrib = embed_to_distrib(gpt_oss, res.hidden_states[-1][:, -1:], logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
    "print()\n",
    "print(source)\n",
    "res = gpt_oss(**inputs[1], output_hidden_states=True)\n",
    "distrib = embed_to_distrib(gpt_oss, res.hidden_states[-1][:, -1:], logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1fc9b577",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_position_config(model_type, component, layer):\n",
    "    config = IntervenableConfig(\n",
    "        model_type=model_type,\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                layer,              # layer\n",
    "                component,          # component\n",
    "                \"pos\",              # intervention unit\n",
    "                1,                  # max number of unit\n",
    "            ),\n",
    "        ],\n",
    "        intervention_types=VanillaIntervention,\n",
    "    )\n",
    "    return config\n",
    "\n",
    "\n",
    "base = inputs[0]\n",
    "sources = [inputs[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cf280b",
   "metadata": {},
   "source": [
    "### Patch Patching on Position-aligned Tokens\n",
    "We path patch on two modules on each layer:\n",
    "- [1] MLP output (the MLP output will be from another example)\n",
    "- [2] MHA input (the self-attention module input will be from another module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "985e8062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# should finish within 1 min with a standard 12G GPU\n",
    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
    "\n",
    "data = []\n",
    "for layer_i in range(gpt_oss.config.num_hidden_layers):\n",
    "    config = simple_position_config(type(gpt_oss), \"mlp_output\", layer_i)\n",
    "    intervenable = IntervenableModel(config, gpt_oss)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        base[\"output_hidden_states\"] = True\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base, sources, {\"sources->base\": pos_i}\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt_oss, counterfactual_outputs.hidden_states[-1][:, -1:], logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"f{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"mlp_output\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "    config = simple_position_config(type(gpt_oss), \"attention_input\", layer_i)\n",
    "    intervenable = IntervenableModel(config, gpt_oss)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        base[\"output_hidden_states\"] = True\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base, sources, {\"sources->base\": pos_i}\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt_oss, counterfactual_outputs.hidden_states[-1][:, -1:], logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"a{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"attention_input\",\n",
    "                }\n",
    "            )\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27180cc7",
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
    "nodes = []\n",
    "for l in range(gpt_oss.config.num_hidden_layers - 1, -1, -1):\n",
    "    nodes.append(f\"f{l}\")\n",
    "    nodes.append(f\"a{l}\")\n",
    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
    "\n",
    "g = (\n",
    "    ggplot(df)\n",
    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
    "    + facet_wrap(\"~token\")\n",
    "    + theme(axis_text_x=element_text(rotation=90))\n",
    ")\n",
    "g"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
